CENTRIST: A Visual Descriptor for Scene Categorization
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Wu, Jianxin
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Abstract
CENTRIST (CENsus TRansform hISTogram), a new visual descriptor for recognizing topological places or scene
categories, is introduced in this paper. We show that place and scene recognition, especially for indoor environments, require its
visual descriptor to possess properties that are different from other vision domains (e.g. object recognition). CENTRIST satisfies these
properties and suits the place and scene recognition task. It is a holistic representation and has strong generalizability for category
recognition. CENTRIST mainly encodes the structural properties within an image and suppresses detailed textural information. Our
experiments demonstrate that CENTRIST outperforms the current state-of-the-art in several place and scene recognition datasets,
compared with other descriptors such as SIFT and Gist. Besides, it is easy to implement and evaluates extremely fast.
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2011-08
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